docstr: Get the scalar from model validation summary.
  
  This method will return a list of summary data of
  [iteration_number, scalar_value, timestamp].
  Note that the metric and tag may not be consistent.
  Please look up following form to pass tag parameter.
  Left side is your metric during compile.
  Right side is the tag you should pass.
  
  >>> 'Accuracy'                  |   'Top1Accuracy'
  >>> 'BinaryAccuracy'            |   'Top1Accuracy'
  >>> 'CategoricalAccuracy'       |   'Top1Accuracy'
  >>> 'SparseCategoricalAccuracy' |   'Top1Accuracy'
  >>> 'AUC'                       |   'AucScore'
  >>> 'HitRatio'                  |   'HitRate@k' (k is Top-k)
  >>> 'Loss'                      |   'Loss'
  >>> 'MAE'                       |   'MAE'
  >>> 'NDCG'                      |   'NDCG'
  >>> 'TFValidationMethod'        |   '${name + " " + valMethod.toString()}'
  >>> 'Top5Accuracy'              |   'Top5Accuracy'
  >>> 'TreeNNAccuracy'            |   'TreeNNAccuracy()'
  >>> 'MeanAveragePrecision'      |   'MAP@k' (k is Top-k) (BigDL)
  >>> 'MeanAveragePrecision'      |   'PascalMeanAveragePrecision' (Zoo)
  >>> 'StatelessMetric'           |   '${name}'
  
  :param tag: The string variable represents the scalar wanted
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code: def get_validation_summary(self, tag=None):
      """
      Get the scalar from model validation summary.
  
      This method will return a list of summary data of
      [iteration_number, scalar_value, timestamp].
      Note that the metric and tag may not be consistent.
      Please look up following form to pass tag parameter.
      Left side is your metric during compile.
      Right side is the tag you should pass.
  
      >>> 'Accuracy'                  |   'Top1Accuracy'
      >>> 'BinaryAccuracy'            |   'Top1Accuracy'
      >>> 'CategoricalAccuracy'       |   'Top1Accuracy'
      >>> 'SparseCategoricalAccuracy' |   'Top1Accuracy'
      >>> 'AUC'                       |   'AucScore'
      >>> 'HitRatio'                  |   'HitRate@k' (k is Top-k)
      >>> 'Loss'                      |   'Loss'
      >>> 'MAE'                       |   'MAE'
      >>> 'NDCG'                      |   'NDCG'
      >>> 'TFValidationMethod'        |   '${name + " " + valMethod.toString()}'
      >>> 'Top5Accuracy'              |   'Top5Accuracy'
      >>> 'TreeNNAccuracy'            |   'TreeNNAccuracy()'
      >>> 'MeanAveragePrecision'      |   'MAP@k' (k is Top-k) (BigDL)
      >>> 'MeanAveragePrecision'      |   'PascalMeanAveragePrecision' (Zoo)
      >>> 'StatelessMetric'           |   '${name}'
  
      :param tag: The string variable represents the scalar wanted
      """
      if self.is_nnframe_fit:
          assert tag is not None, (
              "You should provide tag which should match the name of "
              "the ValidationMethod set into the optimizer. "
              "e.g.'MAE', 'Top1AccuracyLoss', 'Top1Accuracy' or "
              "'Top5Accuracy'."
          )
          val_summary = self.nn_estimator.getValidationSummary()
          return val_summary.read_scalar(tag=tag)
      else:
          return self.estimator.get_validation_summary(tag=tag)
  
imports: from zoo.orca.learn.metrics import Metric
  from zoo.orca.learn.utils import bigdl_metric_results_to_dict
  from zoo.pipeline.nnframes import NNEstimator, NNModel
  from zoo.pipeline.estimator import Estimator
  from zoo.orca.learn.spark_estimator import Estimator
  from zoo.orca.data import SparkXShards
  from bigdl.optim.optimizer import MaxEpoch
  from zoo.feature.common import FeatureSet
  from zoo.orca.learn.metrics import Accuracy
  from pyspark.sql.dataframe import DataFrame
label_desc: Use image node_id 13 for calling the BigDLEstimator obj's underlying member method code with example usage: obj.get_validation_summary(tag) and returns: val_summary, self